Block-based motion estimation is an important element in many video coding standards that aims at removing temporal redundancy between neighboring frames; yet, it is the most computationally expensive stage in traditional video compression methods. For example, traditional methods for block-based motion estimation such as the Exhaustive Block Matching Algorithm (EBMA) or the Full-Search Block Matching Algorithm (FSBMA) are capable of achieving good matching performance, but are computationally expensive. Alternatives to EBMA have been proposed to reduce the amount of search points within a search window by trading off matching optimally with computational resources. Since these algorithms only take into account a sub-region of the search space, they often achieve suboptimal results associated with local maxima.
Although traditional motion estimation algorithms exploit shared local spatial characteristics around the target block, they fail to take advantage of motion parameters that are easily detectable and measurable from video acquired with stationary cameras; in particular, presence/absence of apparent motion and direction of apparent motion can be estimated somewhat accurately and used to expedite the search process. Furthermore, attempts at computationally efficient block-based motion estimation that was both computationally efficient with capabilities to learn dominant spatio-temporal characteristics of motion patterns captured in the video required model construction, maintenance, and storage capabilities that translate into higher storage requirements.
What is needed are means for block-based motion estimation that is computationally efficient and that produces motion vectors that are semantically tuned with the motion in the scene, without significantly sacrificing matching performance relative to exhaustive methods.
In light of prior limitations, the present inventors herein disclose a system and method that provides the advantages of block-based motion estimation, white foregoing motion model construction, update and storage, by estimating local patterns of motion based on incoming frames.